In this exercise, you will load a filtered gapminder
dataset - with a subset of data on global development from 1952 - 2007
in increments of 5 years - to capture the period between the Second
World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.
First, start with installing and activating the relevant packages
tidyverse, gganimate, and
gapminder if you do not have them already. Pay
attention to what warning messages you get when installing
gganimate, as your computer might need other packages than
gifski and av
#install.packages("gganimate")
#install.packages("gifski")
#install.packages("gapminder")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(gganimate)
library(gifski)
library(gapminder)
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
gapminder %>%
filter(year== 1952) %>%
arrange(desc(gdpPercap)) %>%
head(1)
## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(data = subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
ggtitle("1952")
ggplot(subset(gapminder, year == 1952), aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) + # Gør punkterne lettere gennemsigtige
scale_x_log10(labels = scales::label_comma()) + # Brug log10 + normale tal på x-aksen
scale_size(range = c(2, 10)) + # Justér boblestørrelser
labs(title = "GDP per Capita vs Life Expectancy (1952)",
x = "GDP per Capita (USD)",
y = "Life Expectancy (years)",
size = "Population",
color = "Continent") # Originale labels
…
We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
ggtitle("2007")
ggplot(subset(gapminder, year == 2007), aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10(labels = scales::label_comma()) +
scale_size(range = c(2, 10)) +
labs(title = "GDP per Capita vs Life Expectancy (2007)",
x = "GDP per Capita (USD)",
y = "Life Expectancy (years)",
size = "Population",
color = "Continent")
…
scale_x_log10()) on the x axis? (hint: try to comment
it out and observe the result)Fordi vi har en outlier, så bliver grafen meget meget svær at læse og clustered in, altså kompromeret. Derfor bruger man scale x log
ved at benytte chatgpt fandt jeg frem til formlen. gapminder %>% filter(year== 1952) %>% arrange(desc(gdpPercap)) %>% head(1) Det gav mig så svaret Kuwait
Jeg har brugt ChatGPT til dele af følgende, og fundet frem til koden hvorved jeg har fixet grafen
ggplot(subset(gapminder, year == 1952), aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) + geom_point(alpha = 0.7) + # Gør punkterne lettere gennemsigtige scale_x_log10(labels = scales::label_comma()) + # Brug log10 + normale tal på x-aksen scale_size(range = c(2, 10)) + # Justér boblestørrelser labs(title = “GDP per Capita vs Life Expectancy (1952)”, x = “GDP per Capita (USD)”, y = “Life Expectancy (years)”, size = “Population”, color = “Continent”) # Originale labels
ved figur 2 har jeg brugt denne ggplot(subset(gapminder, year == 2007), aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) + geom_point(alpha = 0.7) + scale_x_log10(labels = scales::label_comma()) + scale_size(range = c(2, 10)) + labs(title = “GDP per Capita vs Life Expectancy (2007)”, x = “GDP per Capita (USD)”, y = “Life Expectancy (years)”, size = “Population”, color = “Continent”)
top5_rigeste <- gapminder %>%
filter(year==2007) %>%
arrange(desc(gdpPercap)) %>%
head(5)
print(top5_rigeste)
## # A tibble: 5 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
Dette giver mig så svaret på at top 5 rigeste lande er, 1: Norge, 2:Kuwait, 3:Singapore, 4:USA, 5:Irland udfra mit datasæt er dette min top 5.
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
library(ggplot2)
library(gganimate)
library(gapminder)
library(scales) # For bedre aksemærkning
##
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
##
## discard
## The following object is masked from 'package:readr':
##
## col_factor
theme_set(theme_bw()) # Sæt et rent tema
# Animation med skiftende titel og forbedrede akser
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) + # Gør boblerne lettere gennemsigtige
scale_x_log10(labels = label_comma()) + # Fjern videnskabelig notation på x-aksen
scale_size(range = c(2, 10)) + # Justér boblestørrelser
labs(title = "Year: {frame_time}", # Dynamisk titel, der ændrer sig med årstallet
x = "GDP per Capita (USD)",
y = "Life Expectancy (years)",
size = "Population",
color = "Continent") +
transition_time(year) + # Animation med interpolering
ease_aes('linear') # Gør animationen glat
# Kør animationen
animate(anim2, renderer = gifski_renderer())
transition_states() and transition_time()
functions respectively)ChatGPT har hjulpet med at identificere transition_time(). Jeg har fundet frem til følgende svar, som jeg har smidt ind med spørgsmål 3 koden.
library(ggplot2) library(gganimate) library(gapminder) library(scales)
theme_set(theme_bw())
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop,
color = continent)) + geom_point(alpha = 0.7) +
scale_x_log10(labels = label_comma()) +
scale_size(range = c(2, 10)) +
labs(title = “Year: {frame_time}”,
x = “GDP per Capita (USD)”, y = “Life Expectancy (years)”, size =
“Population”, color = “Continent”) + transition_time(year) +
ease_aes(‘linear’)
animate(anim2, renderer = gifski_renderer())
Jeg har så fået årstallet til at følge datasættet ved at benytte koden labs(title = “year:{frame_time}”)
library(ggplot2)
library(gganimate)
library(gapminder)
library(scales) # For bedre aksemærkning
theme_set(theme_bw()) # Sæt et rent tema for bedre synlighed
# Forbedret animation
anim6 <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) + # Let gennemsigtige bobler for bedre synlighed
scale_x_log10(labels = scales::label_comma()) + # Fjern videnskabelig notation på x-aksen
scale_size_continuous(labels = scales::label_comma()) + #Denne kode fjerner videnskabelig notation for befolkningstallet og viser det som tal med tusind-separatorer
labs(title = "Year: {frame_time}", # Dynamisk titel med årstal
x = "GDP per Capita (USD)",
y = "Life Expectancy (years)",
size = "Population",
color = "Continent") +
theme(
legend.position = "right", # Placerer forklaringen til højre
axis.text.x = element_text(angle = 45, hjust = 1), # Roterer x-aksemærker for bedre læsbarhed
axis.text.y = element_text(size = 10), # Ændrer skriftstørrelse på y-aksen
axis.title = element_text(size = 12), # Ændrer skriftstørrelse på aksetitler
legend.title = element_text(size = 12), # Ændrer skriftstørrelse på legenden
legend.text = element_text(size = 10) # Ændrer skriftstørrelse på tekst i legenden
) +
transition_time(year) + # Animation over tid
ease_aes('linear') # Glidende overgang
# Kør animationen
animate(anim6, renderer = gifski_renderer())
gapminder_unfiltered dataset or
download more historical data at https://www.gapminder.org/data/ ]Ved hjælp af ChatGPT
library(ggplot2)
library(dplyr)
library(gapminder)
# Filtrér data fra 2002 og 2007
data_comparison <- gapminder %>%
filter(year %in% c(2002, 2007))
# Lav et sammenligningsplot
ggplot(data_comparison, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point(aes(size = pop), alpha = 0.7) +
scale_x_log10(labels = scales::label_comma()) +
scale_size_continuous(labels = scales::label_comma()) +
labs(title = "Comparison of 2002 and 2007",
x = "GDP per Capita (USD)", y = "Life Expectancy (Years)",
color = "Continent", size = "Population") +
facet_wrap(~year) + # Opdel i to grafer (et for hvert år)
theme_minimal()
data_2007 <- gapminder %>%
filter(year == 2002) %>%
summarise(avg_gdp = mean(gdpPercap, na.rm = TRUE),
avg_lifeExp = mean(lifeExp, na.rm = TRUE),
total_pop = sum(pop, na.rm = TRUE))
print(data_2007)
## # A tibble: 1 × 3
## avg_gdp avg_lifeExp total_pop
## <dbl> <dbl> <dbl>
## 1 9918. 65.7 5886977579
data_2007 <- gapminder %>%
filter(year == 2007) %>%
summarise(avg_gdp = mean(gdpPercap, na.rm = TRUE),
avg_lifeExp = mean(lifeExp, na.rm = TRUE),
total_pop = sum(pop, na.rm = TRUE))
print(data_2007)
## # A tibble: 1 × 3
## avg_gdp avg_lifeExp total_pop
## <dbl> <dbl> <dbl>
## 1 11680. 67.0 6251013179
Man kan så se at den generelle gdp er steget, så det generelle output pr person er steget, hvilket vil sige at man har fået flere penge mellem hænderne. Derudover kan man også se på den generelle levealder, hvor ud fra datasættet stiger med knap 1.5 år, hvilket er en stor forskel iforhold til hvis man kigger på tallene fra 1997 til 2002, hvor stigningen kun er 0.68 år. Dette viser at der er en stor udvikling i at mennesket lever længere generelt i verdenen. Til slut kan man så også se at befolkningstallet også er steget, hvilket giver mening i forhold til vi er blevet rigere og lever længere, hvilket giver god grund til hvorfor vi så bliver flere. Dog er uligheden stadig en udfordring. Mens nogle lande har oplevet stor vækst, er andre blevet efterladt, især i dele af Afrika, hvor økonomisk fremgang har været langsommere. Samlet set viser dataene, at verden i 2007 var rigere og sundere end i 2002. Der er stadig problemer, men udviklingen går i den rigtige retning.